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from matplotlib import pyplot as plt
import numpy as np
import matplotlib
import matplotlib.patches as patches
import cartopy.crs as ccrs
import cartopy.feature as cfeature
from datetime import datetime
from enstools.feature.util.data_utils import pb_str_to_datetime
from pathlib import Path
import enstools.feature.identification.african_easterly_waves.configuration as cfg
# plots the wave state (all wavetroughs given specific timestep in a set) ts: pb2.Timestep
def plot_wavetroughs(ts, fig_name, cv=None):
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(11, 4), subplot_kw=dict(projection=ccrs.PlateCarree()))
x_ticks = [-100, -95, -85, -75, -65, -55, -45, -35, -25, -15, -5, 5, 15, 25, 35]
y_ticks = [0, 10, 20, 30]
extent = [-100, -45, -10, 35]
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if cv is not None:
levelfc = np.asarray([0, 0.5, 1, 2, 3]) * 1e-5
cv.plot.contourf(levels=levelfc, vmin=0, extend='max', cmap='Blues')
# generate plot per pressure level, per time step
# colors per time step
# min_time = wave_thr_list[0].time.astype('float64')
# max_time = wave_thr_list[-1].time.astype('float64')
# cmap = matplotlib.cm.get_cmap('rainbow')
# color_wgts = np.linspace(0.0, 1.0, len(wave_thr_list))
# colors = ['red', 'yellow', 'green', 'blue', 'purple']
vt = ts.valid_time
for obj_idx, obj in enumerate(ts.objects):
# time64 = wave.time.astype('float64')
# time_weight = (time64 - min_time) / (max_time - min_time) if max_time > min_time else 1.0
line_pts = obj.properties.line_pts
line = patches.Path([[p.lon, p.lat] for p in line_pts])
patch = patches.PathPatch(line, linewidth=2, facecolor='none', edgecolor='red') # cmap(time_weight)
ax.add_patch(patch)
ax.coastlines()
ax.add_feature(cfeature.BORDERS.with_scale('50m'))
ax.set_extent(extent, crs=ccrs.PlateCarree())
yt1 = ax.set_yticks(y_ticks, crs=ccrs.PlateCarree())
xt1 = ax.set_xticks(x_ticks, crs=ccrs.PlateCarree())
figure_name = fig_name.replace(':', '_') + '_aew_troughs.png'
plt.savefig(figure_name, format='png')
plt.figure().clear()
plt.close()
plt.cla()
plt.clf()
return figure_name
def plot_timesteps_from_desc(object_desc, cv=None):
# plot for each set for each timestep everything detected.
from enstools.feature.util.data_utils import get_subset_by_description
for set_idx, od_set in enumerate(object_desc.sets):
cv_set = get_subset_by_description(cv, od_set, '2d')
for ts in od_set.timesteps:
cv_st = cv_set.sel(time=ts.valid_time).cv
fnt = fn + "_" + ts.valid_time
print(fnt)
# ts.validTime / .objects
fout_name = plot_wavetroughs(ts, fnt, cv=cv_st)
def plot_track_from_graph(track_desc, fig_name_prefix, cv=None):
nodes = [edge.parent for edge in track_desc.edges]
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(11, 4), subplot_kw=dict(projection=ccrs.PlateCarree()))
x_ticks = [-100, -95, -85, -75, -65, -55, -45, -35, -25, -15, -5, 5, 15, 25, 35]
y_ticks = [0, 10, 20, 30]
extent = [-100, -45, -10, 35]
if cv is not None:
cv = cv.isel(time=0)
levelfc = np.asarray([0, 0.5, 1, 2, 3]) * 1e-5
cv.plot.contourf(levels=levelfc, vmin=0, extend='max', cmap='Blues')
# generate plot per pressure level, per time step
# colors per time step
min_time = pb_str_to_datetime(nodes[0].time).timestamp()
max_time = pb_str_to_datetime(nodes[-1].time).timestamp()
cmap = matplotlib.cm.get_cmap('rainbow')
color_wgts = np.linspace(0.0, 1.0, len(nodes))
colors = ['red', 'yellow', 'green', 'blue', 'purple']
for node_idx, node in enumerate(nodes):
obj = node.object
time_d = pb_str_to_datetime(node.time).timestamp()
time_weight = (time_d - min_time) / (max_time - min_time) if max_time > min_time else 1.0
line_pts = obj.properties.line_pts
line = patches.Path([[p.lon, p.lat] for p in line_pts])
patch = patches.PathPatch(line, linewidth=2, facecolor='none', edgecolor=cmap(time_weight))
ax.add_patch(patch)
ax.coastlines()
ax.add_feature(cfeature.BORDERS.with_scale('50m'))
ax.set_extent(extent, crs=ccrs.PlateCarree())
yt1 = ax.set_yticks(y_ticks, crs=ccrs.PlateCarree())
xt1 = ax.set_xticks(x_ticks, crs=ccrs.PlateCarree())
figure_name = fig_name_prefix + '_troughs.png' # .replace(':', '_')
plt.title(nodes[0].time + " - " + nodes[-1].time)
print("Plot to " + str(figure_name))
plt.savefig(figure_name, format='png')
plt.figure().clear()
plt.close()
plt.cla()
plt.clf()
return figure_name
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def plot_wt_list(nodes, fn):
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(11, 4), subplot_kw=dict(projection=ccrs.PlateCarree()))
x_ticks = [-100, -95, -85, -75, -65, -55, -45, -35, -25, -15, -5, 5, 15, 25, 35]
y_ticks = [0, 10, 20, 30]
extent = [-100, -45, -10, 35]
# generate plot per pressure level, per time step
# colors per time step
# min_time = wave_thr_list[0].time.astype('float64')
# max_time = wave_thr_list[-1].time.astype('float64')
# cmap = matplotlib.cm.get_cmap('rainbow')
# color_wgts = np.linspace(0.0, 1.0, len(wave_thr_list))
# colors = ['red', 'yellow', 'green', 'blue', 'purple']
for node in nodes:
line_pts = node.object.properties.line_pts
line = patches.Path([[p.lon, p.lat] for p in line_pts])
patch = patches.PathPatch(line, linewidth=2, facecolor='none', edgecolor='red') # cmap(time_weight)
ax.add_patch(patch)
ax.coastlines()
ax.add_feature(cfeature.BORDERS.with_scale('50m'))
ax.set_extent(extent, crs=ccrs.PlateCarree())
yt1 = ax.set_yticks(y_ticks, crs=ccrs.PlateCarree())
xt1 = ax.set_xticks(x_ticks, crs=ccrs.PlateCarree())
figure_name = fn + '.png'
plt.savefig(figure_name, format='png')
plt.figure().clear()
plt.close()
plt.cla()
plt.clf()
return figure_name
def plot_track_in_ts(track):
per_ts_wts = dict()
for edge in track.edges:
node = edge.parent
key = node.time.replace(':', '_')
if key in per_ts_wts:
per_ts_wts[key].append(node)
else:
per_ts_wts[key] = [node]
for time, nodes in per_ts_wts.items():
plot_wt_list(nodes, fn + time)
from enstools.feature.util.data_utils import get_subset_by_description
for set_idx, od_set in enumerate(graph_desc.sets):
# cv_set = get_subset_by_description(ds, od_set, '2d')
for set_tr, track in enumerate(od_set.tracks):
fn = cfg.plot_dir + 'set_' + str(set_idx) + "_track_" + str(set_tr)
plot_track_from_graph(track, fn, cv=None)